Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking

Stability and reproducibility of co-electrospun brain-mimicking phantoms for quality assurance of diffusion MRI sequences

Grey and white matter mimicking phantoms are important for assessing variations in diffusion MR measures at a single time point and over an extended period of time. This work investigates the stability of brain-mimicking microfibre phantoms and reproducibility of their MR derived diffusion parameters. The microfibres were produced by co-electrospinning and characterized by scanning electron microscopy (SEM). Grey matter and white matter phantoms were constructed from random and aligned microfibres, respectively. MR data were acquired from these phantoms over a period of 33 months. SEM images revealed that only small changes in fibre microstructure occurred over 30 months. The coefficient of variation in MR measurements across all time-points was between 1.6% and 3.4% for MD across all phantoms and FA in white matter phantoms. This was within the limits expected for intra-scanner variability, thereby confirming phantom stability over 33 months. These specialised diffusion phantoms may be used in a clinical environment for intra and inter-site quality assurance purposes, and for validation of quantitative diffusion biomarkers

Fast algorithms for fitting active appearance models to unconstrained images

Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen variations. In this paper, we look into AAM fitting algorithms and make the following orthogonal contributions: We present a simple “project-out” optimization framework that unifies and revises the most well-known optimization problems and solutions in AAMs. Based on this framework, we describe robust simultaneous AAM fitting algorithms the complexity of which is not prohibitive for current systems. We then go on one step further and propose a new approximate project-out AAM fitting algorithm which we coin extended project-out inverse compositional (E-POIC). In contrast to current algorithms, E-POIC is both efficient and robust. Next, we describe a part-based AAM employing a translational motion model, which results in superior fitting and convergence properties. We also show that the proposed AAMs, when trained “in-the-wild” using SIFT descriptors, perform surprisingly well even for the case of unseen unconstrained images. Via a number of experiments on unconstrained human and animal face databases, we show that our combined contributions largely bridge the gap between exact and current approximate methods for AAM fitting and perform comparably with state-of-the-art face alignment algorithms

Accounting for predator species identity reveals variable relationships between nest predation rate and habitat in a temperate forest songbird.

Nest predation is the primary cause of nest failure in most ground-nesting bird species. Investigations of relationships between nest predation rate and habitat usually pool different predator species. However, such relationships likely depend on the specific predator involved, partly because habitat requirements vary among predator species. Pooling may therefore impair our ability to identify conservation-relevant relationships between nest predation rate and habitat. We investigated predator-specific nest predation rates in the forest-dependent, ground-nesting wood warbler Phylloscopus sibilatrix in relation to forest area and forest edge complexity at two spatial scales and to the composition of the adjacent habitat matrix. We used camera traps at 559 nests to identify nest predators in five study regions across Europe. When analyzing predation data pooled across predator species, nest predation rate was positively related to forest area at the local scale (1000 m around nest), and higher where proportion of grassland in the adjacent habitat matrix was high but arable land low. Analyses by each predator species revealed variable relationships between nest predation rates and habitat. At the local scale, nest predation by most predators was higher where forest area was large. At the landscape scale (10,000 m around nest), nest predation by buzzards Buteo buteo was high where forest area was small. Predation by pine martens Martes martes was high where edge complexity at the landscape scale was high. Predation by badgers Meles meles was high where the matrix had much grassland but little arable land. Our results suggest that relationships between nest predation rates and habitat can depend on the predator species involved and may differ from analyses disregarding predator identity. Predator-specific nest predation rates, and their relationships to habitat at different spatial scales, should be considered when assessing the impact of habitat change on avian nesting success

Reproductive success of the wood warbler Phylloscopus sibilatrix varies across Europe

Differences in population trends across a species’ breeding range are ultimately linked to variation in demographic rates. In small songbirds, demographic rates related to fecundity typically have strong effects on population trends. Populations of a forest songbird, the wood warbler Phylloscopus sibilatrix, have been declining in many but not all regions of the European breeding range. We investigated if clutch size, hatching rate, nest survival and number of fledglings vary across Europe, and if nest survival is related to differences in the regionally dominant nest predator class (birds versus mammals). From 2009 to 2020, we monitored 1896 nests and used cameras at a subsample of 645 nests in six study regions: the United Kingdom (mid-Wales, Dartmoor, the New Forest), Germany (Hessen), Switzerland (Jura mountains) and Poland (Białowieża National Park). Number of fledglings was lowest in the New Forest (1.43 ± CI 0.23), intermediate in Jura (2.41 ± 0.31) and Białowieża (2.26 ± 0.24) and highest in mid-Wales (3.02 ± 0.48) and Dartmoor (2.92 ± 0.32). The reason for low reproductive success in the New Forest, Jura and Białowieża was low nest survival, and large clutch sizes in Białowieża did not compensate for high nest losses. High reproductive success in mid-Wales and Dartmoor was due to high nest survival and large clutch sizes. Overall predation rates were similar everywhere despite variation between the regions in the dominant nest predator class. Unsuccessful nests in mid-Wales were mainly predated by birds; in Dartmoor, the New Forest, Hessen and Jura similarly by birds and mammals; and in Białowieża exclusively by mammals. Regional reproductive success does not match the population trends recently reported for the wood warbler in the six study regions (i.e. high reproduction ≠ positive trend). Annual survival may be a decisive factor, but it is difficult to quantify for a nomadic species such as the wood warbler that rarely returns to the same breeding locations

Extended Supervised Descent Method for Robust Face Alignment

Abstract. Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating/face alignment. It learns a sequence of descent directions that minimize the difference between the estimated shape and the ground truth in HOG feature space during training, and utilize them in testing to predict shape increment itera-tively. In this paper, we propose to modify SDM in three respects: 1) Multi-scale HOG features are applied orderly as a coarse-to-fine feature detector; 2) Global to local constraints of the facial features are con-sidered orderly in regression cascade; 3) Rigid Regularization is applied to obtain more stable prediction results. Extensive experimental result-s demonstrate that each of the three modifications could improve the accuracy and robustness of the traditional SDM methods. Furthermore, enhanced by the three-fold improvements, the extended SDM compares favorably with other state-of-the-art methods on several challenging face data sets, including LFPW, HELEN and 300 Faces in-the-wild.